1. Field of the Invention
The present invention relates to an image processing apparatus and an image processing method, and particularly relates to an image processing apparatus and an image processing method for carrying out processing with regard to an emotional value (also called “Kansei score”; hereinafter the emotional value is called the “Kansei score”) of an image.
2. Description of the Related Art
What images express is not only information but also affection of human beings, etc. For example, some images may let people associate with warmth; some images may let people feel cold; some images may embody vivid scenes; and some images may look lifeless. The affection of human beings triggered by an image is called “Kansei”. Here it should be noted that even for a same image, different observers may have different realizations of Kansei; also even for a same observer, in different circumstances, he may have different realizations of Kansei with regard to a same image. However, in general, most observers may have a same realization of Kansei with regard to a same image; as a result, the realization of Kansei may be quantified. The value obtained by carrying out quantification with regard to the realization of Kansei is called the “Kansei score”.
With the developments of Internet techniques, many people publish their photos through social networks so as to share their affections, and hope that their expected realization of Kansei may be acquired by other viewers after the photos are viewed. This means that there exists a requirement of quantifying realization of Kansei of an image, i.e., obtaining a Kansei score. Furthermore, in many circumstances, people may find that their photos cannot bring their expected realization of Kansei to other viewers. As a result, before an image is published, it may be necessary to adjust the image so as to let the Kansei score of the image be equal to or at least approach the expected Kansei score. However, at present, there does not exist a means of estimating a Kansei score of an image, better suited for an ordinary user (i.e., a non-expert of image processing). In addition, even in a case where the user knows his expected Kansei score of an image, there also does not exist a simple and automatic means by which the user may adjust the Kansei score of the image to be equal to or approach the expected Kansei score.
The below cited reference No. 1 proposes an image enhancement means. According to selection of an emotional expression such as “sharp” or “mild”, an editing command creating device in a personal computer creates editing commands for image data. Parameters and steps of image processing are adjusted by an input state setting device based on features of a display unit of the personal computer, and then the processed image is displayed on the display unit. The created editing commands are transmitted to a laboratory to serve as overall emotional expression scripts. In the laboratory, according to the overall emotional expression scripts, the image processing is carried out with regard to the image data. At this time, an output state setting device changes the parameters of the image processing according to features of an output medium. The processed image data is output, by an output device, to the corresponding output medium. In this image enhancement means, as for a problem that realization of Kansei of a same image shown on different display units may be different due to innate features of the different display units, a user may press, for example, a “sharp” button to let the image look sharp; in order to let the image achieve a good effect, the button may need to be pressed many times. However, this image enhancement means does not relate to the Kansei score, and does not try to quantify the realization of Kansei of the image. In other words, this image enhancement means just carries out calculation and adjustment processing with regard to concrete image feature parameters, and is a semi-automatic means. As a result, the user needs to perform operation many times (for example, to press a button many times) in order to approach an expected result.
The below cited reference No. 2 provides a means of classifying images into various Kansei types. This means comprises a step of carrying out multi-resolution expression with regard to an image; a step of carrying out image synthesis by integrating plural high-frequency sample images; a step of creating a histogram of the synthesized image; and a step of carrying out image classification according to the histogram. The means in this reference tries to build a direct mapping relationship between an image histogram and image Kansei, i.e., tries to build direct mapping from image features to Kansei realization, so as to assign a Kansei type to an image; however, since Kansei of an image is delicate and complicated, the built direct mapping from the image features to the Kansei realization is almost always incorrect. In addition, the means in this reference also does not relate to carrying out quantization with regard to Kansei realization of an image; for example, although an image is classified as “warm”, it is not provided how warm the image is, i.e., a Kansei score with regard to warm is not provided.    Cited Reference No. 1: U.S. Pat. No. 7,170,638 B2    Cited Reference No. 2: US Patent Application Publication No. 2010/0074523 A1